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Neural network based Fault Tolerant System for Cascaded Multilevel Inverters

Neural network based Fault Tolerant System for Cascaded Multilevel Inverters


1. Understanding the Fault Tolerance Concept

Overview of Cascaded Multilevel Inverters

  1. Cascaded Multilevel Inverter Configuration

  • For our 15-level Cascaded Multilevel Inverter (CMLI), we require seven H-bridge inverters (H1, H2, H3, H4, H5, H6, and H7).

  • Each H-bridge is responsible for generating a specific level of output voltage.

  1. Types of Faults

  • Inverter Failure: Complete failure of an H-bridge inverter.

  • Switch Failure: Individual switches within the inverter may fail.

  • Battery Failure (DC Source Failure): Issues with the DC power source affecting inverter operation.

  • These faults need to be detected and corrected to maintain stable output voltage.



Fault Detection and Correction

  1. Neural Network for Fault Detection

  • A neural network will be trained to detect faults based on the voltage measurements across each H-bridge and the load.

  • The network will identify which specific H-bridge is failing and trigger appropriate corrective actions.

  1. Corrective Actions

  • When a fault is detected, a backup H-bridge inverter will be activated to compensate and maintain the required output voltage.

2. Implementing the Fault Tolerance System

Simulink Model Setup

  1. Building the Model

  • Create a Simulink model with seven H-bridge inverters.

  • Incorporate ideal switches and constants to simulate normal and fault conditions.

  1. Fault Creation

  • Implement fault conditions by changing constants to simulate inverter failures.

  • Include an additional H-bridge inverter (Aary H-bridge) that will be activated during fault conditions to maintain output voltage.

Data Collection for Neural Network Training

  1. Gathering Data

  • Measure voltages across each H-bridge and the load under normal and fault conditions.

  • Collect data for various scenarios, including normal operation and faults in different H-bridges.

  1. Preparing Data for Training

  • Create a dataset with both normal and fault conditions.

  • Generate a significant amount of data (e.g., 10 sets of samples) for effective neural network training.

  1. Labeling Data

  • Assign labels to the data indicating the specific fault or normal condition.

  • For example, if a fault occurs in H-bridge 3, label the data accordingly.

3. Training the Neural Network

Neural Network Training Process

  1. Setting Up the Neural Network

  • Use MATLAB's Neural Network Toolbox to train the neural network.

  • Input data includes voltage measurements, and output data includes fault classification.

  1. Training and Validation

  • Train the neural network using the collected data.

  • Validate the network to ensure accurate fault detection and classification.

  • Ensure that the network output matches the expected results for various fault conditions.

4. Simulation and Results

Running the Simulation

  1. Simulating Normal Operation

  • Run the model with all toggles set to simulate normal operation.

  • Verify that the system outputs the correct 15-level voltage.

  1. Simulating Fault Conditions

  • Introduce faults in various H-bridges (e.g., H-bridge 1, 2, 3, etc.).

  • Observe the network’s ability to detect faults and activate the backup H-bridge inverter.

Results and Analysis

  1. Fault Detection

  • The neural network accurately detects faults and activates the backup inverter as needed.

  • The output voltage remains stable at the required level despite faults.

  1. System Performance

  • The fault tolerance system maintains the 15-level output voltage even when faults occur.

  • The neural network effectively identifies faults and ensures continuous power supply to the load.

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